Revealed Wisdom of the Crowd: Bids Predict Loan Quality

• 成果类型: Article; Early Access
• 署名作者: Yao, Jiayu; Lin, Mingfeng; Wu, D. J.
• 署名单位: Nanyang Technological University; University System of Georgia; Georgia Institute of Technology
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2022.02575
• 发表日期: 2025
• 关键词: Wisdom of the crowd; platform; Crowdfunding; FinTech

摘要

Despite the popularity of the phrase wisdom of the crowd, not all crowds are wise because not everyone in them acts in an informed, rational manner. Identifying informative actions, therefore, can help to isolate the truly wise part of a crowd. Motivated by this idea, we evaluate the informational value of investors' bids using data from online, debt-based crowdfunding, in which we were able to track both investment decisions and ultimate repayment statuses for individual loans. We propose several easily scalable variables derived from the heterogeneity of investors' bids in terms of size and timing. We first show that loans funded with larger bids relative to the typical bid amount in the market or to the bidder's historical baseline, particularly early in the bidding period, are less likely to default. More importantly, we perform theory-driven feature engineering and find that these variables improve the predictive performance of state-of-the-art models that have been proposed in this context. Even during the fundraising process, these variables improve predictions of both funding likelihood and loan quality. We discuss the implications of these variables, including loan pricing in secondary markets, crowd wisdom in different market mechanisms, and financial inclusion. Crowdfunding platforms can easily implement these variables to improve market efficiency without compromising investor privacy.